Semi-supervised learning uses both labeled data and unlabeled data during training. The domain adaptation problem is similar to semi-supervised learning in the strict sense, but the source domain and target domain have domain shift due to various image capture devices, environmental changes, and different styles.
Initially, the application of a time series ensembling is proposed by [1]}, which adopts the average of the current model prediction results and the historical prediction results to calculate the mean square error. Different from historically weighted sum of model predictions in [1]} , [3]} uses weighted exponential moving average (EMA) on the weight of the student model. Temporal Ensembling [1]} is applied by [5]} to the domain adaptation problem, and data augmentation is implemented to increase the generalization ability of the model. The prediction results of time ensembling is utilized as pseudo-labels to cluster and align the feature spaces of the source domain and the target domain in [6]}.
